首页|Reports from Tongji University Advance Knowledge in Machine Learning (Machine Le arning-based Morphological and Mechanical Prediction of Kirigami-inspired Active Composites)
Reports from Tongji University Advance Knowledge in Machine Learning (Machine Le arning-based Morphological and Mechanical Prediction of Kirigami-inspired Active Composites)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting originating from Shanghai, People’s Repu blic of China, by NewsRx correspondents, research stated, “Kirigami-inspired des igns hold great potential for the development of functional materials and device s, but predicting the morphological configuration of these structures under vari ous loading conditions remains a challenge for traditional experimental and nume rical methods. Here, we present a novel approach that utilizes machine learning algorithms to accurately predict the deformation and stress field of kirigami-in spired programmable active composites.”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningTongji University
NETL
NSTL
Elsevier
